Deep Learning Methods for Filter Extraction.
ABSTRACT
With the exponential growth of the information technology, nowadays tremendous amounts of data including images, audio, text and videos, up to millions or billions, are collected for training machine learning models. Deep neural networks (DNNs) are one of the widely used methods today. Large companies in the uses these methods to recommends buyers with products, filter junk email or text-based hate speeches, understand and translate major languages in real time, and so on.
Inspired by the trend, our work is dedicated to developing and training a deep neural network to extract meaningful patterns from a set of labelled data i.e. making generalizations. We show that DNNs can learn feature representations that can be successfully applied in a wide spectrum of application domains. We show how DNNs are applied to classification problems – grading of fresh tomato fruits based on their physical qualities using supervised learning approach.
TABLE OF CONTENTS
CERTIFICATION …………………………………………………………………………………………………………ii
ABSTRACT…………………………………………………………………………………………………………………v
ACKNOWLEDGEMENTS …………………………………………………………………………………………….vi
DEDICATION……………………………………………………………………………………………………………. vii
LIST OF FIGURES ………………………………………………………………………………………………………x
LIST OF ABBREVIATIONS…………………………………………………………………………………………..xi
CHAPTER ONE INTRODUCTION……………………………………………………………………………. 1
1.1 Background…………………………………………………………………………………………………. 1
1.1.1 Classifications …………………………………………………………………………………………. 2
1.1.2 Deep Learning and Neural Networks…………………………………………………………… 2
1.1.3 Mocha Package……………………………………………………………………………………….. 3
1.1.4 Julia Programming Language…………………………………………………………………….. 5
1.1.5 Tomato Fruit……………………………………………………………………………………………. 5
1.2 Motivation and Research Objectives:………………………………………………………………. 6
1.3 The Scope of the Research Work …………………………………………………………………… 7
1.4 Importance………………………………………………………………………………………………….. 7
CHAPTER TWO REVIEW OF RELATED LITERATURE………………………………………………. 9
2.1 Deep Learning …………………………………………………………………………………………….. 9
2.2 Feature Extractions………………………………………………………………………………………11
2.2.1 Continuous Convolution…………………………………………………………………………….15
2.2.2 Discrete Convolution ………………………………………………………………………………..16
2.2.3 Convolutional Neural Network Concepts ……………………………………………………..16
CHAPTER THREE RELATED WORKS ON APPLICATION OF CLASSIFICATIONS …………..21
CHAPTER FOUR RESEARCH METHODOLOGY ………………………………………………………..26
4.1 Deep Learning Toolkits / Libraries and Architecture …………………………………………..26
4.1.1 Mocha Package……………………………………………………………………………………….26
4.2 Dataset Compilation……………………………………………………………………………………..26
4.2.1 Collection of Data …………………………………………………………………………………….26
4.2.2 Data Preparation ……………………………………………………………………………………..27
4.2.3 Instruments Used …………………………………………………………………………………….31
4.3 Defining Network Architecture………………………………………………………………………..31
4.4 Training and Evaluation ………………………………………………………………………………..32
4.4.1 Evaluation……………………………………………………………………………………………….33
CHAPTER FIVE EXPERIMENT AND RESULTS………………………………………………………..34
CHAPTER SIX DISCUSSION AND RECOMMENDATIONS……………………………………….37
REFERENCES ………………………………………………………………………………………………………….38
INTRODUCTION
Artificial Intelligence (AI) is transitioning from being our daily helper to something much more powerful – and disruptive – as the new machines are rapidly outperforming the most talented of us in many endeavours (Frank, Roehrig, & Pring, 2017). The branch of AI concerned with the study and design of computer programs that automatically improve with experience is called machine learning or ML.
The applications of machine continue to explode in the recent years in the field of agriculture, ecommerce, health, banking, news, robotics, transportation, weather forecasts, software, industries and many more. YouTube uses ML algorithms to suggest videos, Facebook fills newsfeeds, Netflix recommends films and Google search auto-completes texts. We probably interact with machines frequently.
They are used in many of the software programs that we use, such as Microsoft’s infamous (and long abandoned) paperclip in Office (maybe not the most positive example), spam filters, voice recognition software, and lots of computer games (Marsland, 2015). “Your cell phone is chock full of learning algorithms. They are hard at work correcting your typos, understanding your spoken commands, reducing transmission errors, recognizing bar codes and much else.
Your phone can even anticipate what you are going to do next, and advise you accordingly. For example, as you are finishing lunch it discreetly alerts you that your afternoon meeting with an out-of-town visitor will have to start late, because her flight has been delayed”.
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StudentsandScholarship Team.
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